diff --git a/README.md b/README.md
index 8e424adcc04250732f534b351f7ec2dd631dd201..1198c6ae377519ae595e2609b053eb91a7bba01e 100644
--- a/README.md
+++ b/README.md
@@ -28,6 +28,7 @@ install the geo packages. For special instructions to install MLAir on the Jueli
and use it without installation (beside the requirements)
* or download the distribution file (?? .whl) and install it via `pip install <??>`. In this case, you can simply
import MLAir in any python script inside your virtual environment using `import mlair`.
+
# How to start with MLAir
@@ -51,11 +52,15 @@ INFO: DefaultWorkflow started
INFO: ExperimentSetup started
INFO: Experiment path is: /home/<usr>/mlair/testrun_network
...
-INFO: load data for DEBW001 from JOIN
+INFO: load data for DEBW107 from JOIN
+INFO: load data for DEBY081 from JOIN
+INFO: load data for DEBW013 from JOIN
+INFO: load data for DEBW076 from JOIN
+INFO: load data for DEBW087 from JOIN
...
INFO: Training started
...
-INFO: DefaultWorkflow finished after 00:00:12 (hh:mm:ss)
+INFO: DefaultWorkflow finished after 0:03:04 (hh:mm:ss)
```
## Example 2
@@ -82,10 +87,12 @@ INFO: ExperimentSetup started
...
INFO: load data for DEBW030 from JOIN
INFO: load data for DEBW037 from JOIN
+INFO: load data for DEBW031 from JOIN
+INFO: load data for DEBW015 from JOIN
...
INFO: Training started
...
-INFO: DefaultWorkflow finished after 00:00:24 (hh:mm:ss)
+INFO: DefaultWorkflow finished after 00:02:03 (hh:mm:ss)
```
## Example 3
@@ -107,15 +114,15 @@ window_history_size = 14
mlair.run(stations=stations,
window_history_size=window_history_size,
create_new_model=False,
- trainable=False)
+ train_model=False)
```
We can see from the terminal that no training was performed. Analysis is now made on the new stations.
```log
INFO: DefaultWorkflow started
...
-INFO: No training has started, because trainable parameter was false.
+INFO: No training has started, because train_model parameter was false.
...
-INFO: DefaultWorkflow finished after 00:00:06 (hh:mm:ss)
+INFO: DefaultWorkflow finished after 0:01:27 (hh:mm:ss)
```
@@ -222,18 +229,14 @@ behaviour.
```python
from mlair import AbstractModelClass
-import keras
class MyCustomisedModel(AbstractModelClass):
def __init__(self, input_shape: list, output_shape: list):
+ # set attributes shape_inputs and shape_outputs
super().__init__(input_shape[0], output_shape[0])
- # settings
- self.dropout_rate = 0.1
- self.activation = keras.layers.PReLU
-
# apply to model
self.set_model()
self.set_compile_options()
@@ -254,34 +257,36 @@ class MyCustomisedModel(AbstractModelClass):
`self._output_shape` and storing the model as `self.model`.
```python
+import keras
+from keras.layers import PReLU, Input, Conv2D, Flatten, Dropout, Dense
+
class MyCustomisedModel(AbstractModelClass):
def set_model(self):
- x_input = keras.layers.Input(shape=self._input_shape)
- x_in = keras.layers.Conv2D(32, (1, 1), padding='same', name='{}_Conv_1x1'.format("major"))(x_input)
- x_in = self.activation(name='{}_conv_act'.format("major"))(x_in)
- x_in = keras.layers.Flatten(name='{}'.format("major"))(x_in)
- x_in = keras.layers.Dropout(self.dropout_rate, name='{}_Dropout_1'.format("major"))(x_in)
- x_in = keras.layers.Dense(16, name='{}_Dense_16'.format("major"))(x_in)
- x_in = self.activation()(x_in)
- x_in = keras.layers.Dense(self._output_shape, name='{}_Dense'.format("major"))(x_in)
- out_main = self.activation()(x_in)
- self.model = keras.Model(inputs=x_input, outputs=[out_main])
+ x_input = Input(shape=self._input_shape)
+ x_in = Conv2D(4, (1, 1))(x_input)
+ x_in = PReLU()(x_in)
+ x_in = Flatten()(x_in)
+ x_in = Dropout(0.1)(x_in)
+ x_in = Dense(16)(x_in)
+ x_in = PReLU()(x_in)
+ x_in = Dense(self._output_shape)(x_in)
+ out = PReLU()(x_in)
+ self.model = keras.Model(inputs=x_input, outputs=[out])
```
* Your are free how to design your model. Just make sure to save it in the class attribute model.
* Additionally, set your custom compile options including the loss definition.
```python
+from keras.losses import mean_squared_error as mse
+
class MyCustomisedModel(AbstractModelClass):
def set_compile_options(self):
self.initial_lr = 1e-2
self.optimizer = keras.optimizers.SGD(lr=self.initial_lr, momentum=0.9)
- self.lr_decay = mlair.model_modules.keras_extensions.LearningRateDecay(base_lr=self.initial_lr,
- drop=.94,
- epochs_drop=10)
- self.loss = keras.losses.mean_squared_error
+ self.loss = mse
self.compile_options = {"metrics": ["mse", "mae"]}
```
@@ -302,6 +307,15 @@ class MyCustomisedModel(AbstractModelClass):
self.loss = keras.losses.mean_squared_error
self.compile_options = {"optimizer" = keras.optimizers.Adam()}
```
+
+## How to plug in the customised model into the workflow?
+* Make use of the `model` argument and pass `MyCustomisedModel` when instantiating a workflow.
+```python
+from mlair.workflows import DefaultWorkflow
+
+workflow = DefaultWorkflow(model=MyCustomisedModel)
+workflow.run()
+```
## Specials for Branched Models
diff --git a/docs/_source/customise.rst b/docs/_source/customise.rst
index 3d3873cb6c08d0ad6bfcfeda0fe415807252bfff..cb77eb63f8bf1a53d54ca2e0f80fd9bdeb93a0b0 100644
--- a/docs/_source/customise.rst
+++ b/docs/_source/customise.rst
@@ -143,6 +143,20 @@ How to create a customised model?
self.compile_options = {"optimizer" = keras.optimizers.Adam()}
+How to plug in the customised model into the workflow?
+~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
+
+* Make use of the :py:`model` argument and pass :py:`MyCustomisedModel` when instantiating a workflow.
+
+
+.. code-block:: python
+
+ from mlair.workflows import DefaultWorkflow
+
+ workflow = DefaultWorkflow(model=MyCustomisedModel)
+ workflow.run()
+
+
Specials for Branched Models
~~~~~~~~~~~~~~~~~~~~~~~~~~~~